
import pandas as pd
import numpy as np
import plotly.express as px
import seaborn as sns
import matplotlib.pyplot as plt


pd.options.display.float_format = '{:,.2f}'.format


df_data = pd.read_csv('nobel_prize_data.csv')
df_data.head()


# no. of rows and columns
df_data.shape

(962, 16)


# column names
df_data.columns

Index(['year', 'category', 'prize', 'motivation', 'prize_share',
       'laureate_type', 'full_name', 'birth_date', 'birth_city',
       'birth_country', 'birth_country_current', 'sex', 'organization_name',
       'organization_city', 'organization_country', 'ISO'],
      dtype='object')


# sort the award by year (from earliest)
df_data.sort_values('year')


df_data.duplicated().any()

False


df_data.isna().sum()

year                       0
category                   0
prize                      0
motivation                88
prize_share                0
laureate_type              0
full_name                  0
birth_date                28
birth_city                31
birth_country             28
birth_country_current     28
sex                       28
organization_name        255
organization_city        255
organization_country     254
ISO                       28
dtype: int64


col_subset = ['year','category', 'laureate_type',
              'birth_date','full_name', 'organization_name']
df_data.loc[df_data.birth_date.isna()][col_subset]


col_subset = ['year','category', 'laureate_type','full_name', 'organization_name']
df_data.loc[df_data.organization_name.isna()][col_subset]


df_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 962 entries, 0 to 961
Data columns (total 16 columns):
 #   Column                 Non-Null Count  Dtype 
---  ------                 --------------  ----- 
 0   year                   962 non-null    int64 
 1   category               962 non-null    object
 2   prize                  962 non-null    object
 3   motivation             874 non-null    object
 4   prize_share            962 non-null    object
 5   laureate_type          962 non-null    object
 6   full_name              962 non-null    object
 7   birth_date             934 non-null    object
 8   birth_city             931 non-null    object
 9   birth_country          934 non-null    object
 10  birth_country_current  934 non-null    object
 11  sex                    934 non-null    object
 12  organization_name      707 non-null    object
 13  organization_city      707 non-null    object
 14  organization_country   708 non-null    object
 15  ISO                    934 non-null    object
dtypes: int64(1), object(15)
memory usage: 120.4+ KB


df_data.birth_date = pd.to_datetime(df_data.birth_date)


separated_values = df_data.prize_share.str.split('/', expand=True)  # Expand the split strings into separate columns
numerator = pd.to_numeric(separated_values[0])
denomenator = pd.to_numeric(separated_values[1])
df_data['share_pct'] = numerator / denomenator
df_data.share_pct

0     1.00
1     1.00
2     1.00
3     0.50
4     0.50
      ... 
957   0.33
958   1.00
959   0.25
960   0.25
961   0.50
Name: share_pct, Length: 962, dtype: float64


df_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 962 entries, 0 to 961
Data columns (total 17 columns):
 #   Column                 Non-Null Count  Dtype         
---  ------                 --------------  -----         
 0   year                   962 non-null    int64         
 1   category               962 non-null    object        
 2   prize                  962 non-null    object        
 3   motivation             874 non-null    object        
 4   prize_share            962 non-null    object        
 5   laureate_type          962 non-null    object        
 6   full_name              962 non-null    object        
 7   birth_date             934 non-null    datetime64[ns]
 8   birth_city             931 non-null    object        
 9   birth_country          934 non-null    object        
 10  birth_country_current  934 non-null    object        
 11  sex                    934 non-null    object        
 12  organization_name      707 non-null    object        
 13  organization_city      707 non-null    object        
 14  organization_country   708 non-null    object        
 15  ISO                    934 non-null    object        
 16  share_pct              962 non-null    float64       
dtypes: datetime64[ns](1), float64(1), int64(1), object(14)
memory usage: 127.9+ KB


biology = df_data.sex.value_counts()
fig = px.pie(labels=biology.index, 
             values=biology.values,
             title="Percentage of Male vs. Female Winners",
             names=biology.index,
             hole=0.4,)
 
fig.update_traces(textposition='inside', textfont_size=15, textinfo='percent')
 
fig.show()


is_winner = df_data.duplicated(subset=['full_name'], keep=False)
multiple_winners = df_data[is_winner]
print(f'There are {multiple_winners.full_name.nunique()}' \
      ' winners who were awarded the prize more than once.')

There are 6 winners who were awarded the prize more than once.


col_subset = ['year', 'category', 'laureate_type', 'full_name']
multiple_winners[col_subset]


df_data.category.nunique()

6


prizes_per_category = df_data.category.value_counts()
v_bar = px.bar(
        x = prizes_per_category.index,
        y = prizes_per_category.values,
        color = prizes_per_category.values,
        color_continuous_scale='Aggrnyl',
        title='Number of Prizes Awarded per Category')
 
v_bar.update_layout(xaxis_title='Nobel Prize Category', 
                    coloraxis_showscale=False,
                    yaxis_title='Number of Prizes')
v_bar.show()


cat_men_women = df_data.groupby(['category', 'sex'], 
                               as_index=False).agg({'prize': pd.Series.count})
cat_men_women.sort_values('prize', ascending=False, inplace=True)


v_bar_split = px.bar(x = cat_men_women.category,
                     y = cat_men_women.prize,
                     color = cat_men_women.sex,
                     title='Number of Prizes Awarded per Category split by Men and Women')
 
v_bar_split.update_layout(xaxis_title='Nobel Prize Category', 
                          yaxis_title='Number of Prizes')
v_bar_split.show()


prize_per_year = df_data.groupby(by='year').count().prize 
moving_average = prize_per_year.rolling(window=5).mean()
np.arange(1900, 2021, step=5)

array([1900, 1905, 1910, 1915, 1920, 1925, 1930, 1935, 1940, 1945, 1950,
       1955, 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005,
       2010, 2015, 2020])


plt.figure(figsize=(16,8), dpi=200)
plt.title('Number of Nobel Prizes Awarded per Year', fontsize=18)
plt.yticks(fontsize=14)
plt.xticks(ticks=np.arange(1900, 2021, step=5), 
           fontsize=14, 
           rotation=45)
 
ax = plt.gca() # get current axis
ax.set_xlim(1900, 2020)
 
ax.scatter(x=prize_per_year.index, 
           y=prize_per_year.values, 
           c='dodgerblue',
           alpha=0.7,
           s=100,)
 
ax.plot(prize_per_year.index, 
        moving_average.values, 
        c='crimson', 
        linewidth=3,)
 
plt.show()


yearly_avg_share = df_data.groupby(by='year').agg({'share_pct': pd.Series.mean})
share_moving_average = yearly_avg_share.rolling(window=5).mean()


plt.figure(figsize=(16,8), dpi=200)
plt.title('Number of Nobel Prizes Awarded per Year', fontsize=18)
plt.yticks(fontsize=14)
plt.xticks(ticks=np.arange(1900, 2021, step=5), 
           fontsize=14, 
           rotation=45)
 
ax1 = plt.gca()
ax2 = ax1.twinx()
ax1.set_xlim(1900, 2020)
 
# invert the second axis
ax2.invert_yaxis()
 
ax1.scatter(x=prize_per_year.index, 
           y=prize_per_year.values, 
           c='dodgerblue',
           alpha=0.7,
           s=100,)
 
ax1.plot(prize_per_year.index, 
        moving_average.values, 
        c='crimson', 
        linewidth=3,)

# Adding prize share plot on second axis
ax2.plot(prize_per_year.index, 
        share_moving_average.values, 
        c='grey', 
        linewidth=3,)
 
plt.show()


top_countries = df_data.groupby(['birth_country_current'], 
                                  as_index=False).agg({'prize': pd.Series.count})
 
top_countries.sort_values(by='prize', inplace=True)
top20_countries = top_countries[-20:]


h_bar = px.bar(x=top20_countries.prize,
               y=top20_countries.birth_country_current,
               orientation='h',
               color=top20_countries.prize,
               color_continuous_scale='Viridis',
               title='Top 20 Countries by Number of Prizes')
 
h_bar.update_layout(xaxis_title='Number of Prizes', 
                    yaxis_title='Country',
                    coloraxis_showscale=False)
h_bar.show()


df_countries = df_data.groupby(['birth_country_current', 'ISO'], 
                               as_index=False).agg({'prize': pd.Series.count})
df_countries.sort_values('prize', ascending=False)


world_map = px.choropleth(df_countries,
                          locations='ISO',
                          color='prize', 
                          hover_name='birth_country_current', 
                          color_continuous_scale=px.colors.sequential.matter)
 
world_map.update_layout(coloraxis_showscale=True,)
 
world_map.show()


cat_country = df_data.groupby(['birth_country_current', 'category'], 
                               as_index=False).agg({'prize': pd.Series.count})
cat_country.sort_values(by='prize', ascending=False, inplace=True)


merged_df = pd.merge(cat_country, top20_countries, on='birth_country_current')
# change column names
merged_df.columns = ['birth_country_current', 'category', 'cat_prize', 'total_prize'] 
merged_df.sort_values(by='total_prize', inplace=True)


cat_cntry_bar = px.bar(x=merged_df.cat_prize,
                       y=merged_df.birth_country_current,
                       color=merged_df.category,
                       orientation='h',
                       title='Top 20 Countries by Number of Prizes and Category')
 
cat_cntry_bar.update_layout(xaxis_title='Number of Prizes', 
                            yaxis_title='Country')
cat_cntry_bar.show()


prize_by_year = df_data.groupby(by=['birth_country_current', 'year'], as_index=False).count()
prize_by_year = prize_by_year.sort_values('year')[['year', 'birth_country_current', 'prize']]


cumulative_prizes = prize_by_year.groupby(by=['birth_country_current',
                                              'year']).sum().groupby(level=[0]).cumsum()
cumulative_prizes.reset_index(inplace=True)


l_chart = px.line(cumulative_prizes,
                  x='year', 
                  y='prize',
                  color='birth_country_current',
                  hover_name='birth_country_current')
 
l_chart.update_layout(xaxis_title='Year',
                      yaxis_title='Number of Prizes')
 
l_chart.show()


top20_orgs = df_data.organization_name.value_counts()[:20]
top20_orgs.sort_values(ascending=True, inplace=True)


org_bar = px.bar(x = top20_orgs.values,
                 y = top20_orgs.index,
                 orientation='h',
                 color=top20_orgs.values,
                 color_continuous_scale=px.colors.sequential.haline,
                 title='Top 20 Research Institutions by Number of Prizes')
 
org_bar.update_layout(xaxis_title='Number of Prizes', 
                      yaxis_title='Institution',
                      coloraxis_showscale=False)
org_bar.show()


top20_org_cities = df_data.organization_city.value_counts()[:20]
top20_org_cities.sort_values(ascending=True, inplace=True)
city_bar2 = px.bar(x = top20_org_cities.values,
                  y = top20_org_cities.index,
                  orientation='h',
                  color=top20_org_cities.values,
                  color_continuous_scale=px.colors.sequential.Plasma,
                  title='Which Cities Do the Most Research?')
 
city_bar2.update_layout(xaxis_title='Number of Prizes', 
                       yaxis_title='City',
                       coloraxis_showscale=False)
city_bar2.show()


top20_cities = df_data.birth_city.value_counts()[:20]
top20_cities.sort_values(ascending=True, inplace=True)
city_bar = px.bar(x=top20_cities.values,
                  y=top20_cities.index,
                  orientation='h',
                  color=top20_cities.values,
                  color_continuous_scale=px.colors.sequential.Plasma,
                  title='Where were the Nobel Laureates Born?')
 
city_bar.update_layout(xaxis_title='Number of Prizes', 
                       yaxis_title='City of Birth',
                       coloraxis_showscale=False)
city_bar.show()


country_city_org = df_data.groupby(by=['organization_country', 
                                       'organization_city', 
                                       'organization_name'], as_index=False).agg({'prize': pd.Series.count})
 
country_city_org = country_city_org.sort_values('prize', ascending=False)
country_city_org


burst = px.sunburst(country_city_org, 
                    path=['organization_country', 'organization_city', 'organization_name'], 
                    values='prize',
                    title='Where do Discoveries Take Place?',
                   )
 
burst.update_layout(xaxis_title='Number of Prizes', 
                    yaxis_title='City',
                    coloraxis_showscale=False)
 
burst.show()


birth_years = df_data.birth_date.dt.year
df_data['winning_age'] = df_data.year - birth_years


display(df_data.nlargest(n=1, columns='winning_age'))
display(df_data.nsmallest(n=1, columns='winning_age'))


# Descriptive statistics 
df_data.winning_age.describe()

count   934.00
mean     59.95
std      12.62
min      17.00
25%      51.00
50%      60.00
75%      69.00
max      97.00
Name: winning_age, dtype: float64


plt.figure(figsize=(8, 4), dpi=200)
sns.histplot(data=df_data,
             x=df_data.winning_age,
             bins=30)
plt.xlabel('Age')
plt.title('Distribution of Age on Receipt of Prize')
plt.show()


plt.figure(figsize=(8,4), dpi=200)
with sns.axes_style("whitegrid"):
    sns.regplot(data=df_data,
                x='year',
                y='winning_age',
                lowess=True, 
                scatter_kws = {'alpha': 0.4},
                line_kws={'color': 'black'})
 
plt.show()


plt.figure(figsize=(8,4), dpi=200)
with sns.axes_style("whitegrid"):
    sns.boxplot(data=df_data,
                x='category',
                y='winning_age')
 
plt.show()


with sns.axes_style('whitegrid'):
    sns.lmplot(data=df_data,
               x='year', 
               y='winning_age',
               row = 'category',
               lowess=True, 
               aspect=2,
               scatter_kws = {'alpha': 0.6},
               line_kws = {'color': 'black'},)
 
plt.show()


with sns.axes_style("whitegrid"):
    sns.lmplot(data=df_data,
               x='year',
               y='winning_age',
               hue='category',
               lowess=True, 
               aspect=2,
               scatter_kws={'alpha': 0.5},
               line_kws={'linewidth': 5})
 
plt.show()

	year	category	prize	motivation	prize_share	laureate_type	full_name	birth_date	birth_city	birth_country	birth_country_current	sex	organization_name	organization_city	organization_country	ISO
0	1901	Chemistry	The Nobel Prize in Chemistry 1901	"in recognition of the extraordinary services ...	1/1	Individual	Jacobus Henricus van 't Hoff	1852-08-30	Rotterdam	Netherlands	Netherlands	Male	Berlin University	Berlin	Germany	NLD
1	1901	Literature	The Nobel Prize in Literature 1901	"in special recognition of his poetic composit...	1/1	Individual	Sully Prudhomme	1839-03-16	Paris	France	France	Male	NaN	NaN	NaN	FRA
2	1901	Medicine	The Nobel Prize in Physiology or Medicine 1901	"for his work on serum therapy, especially its...	1/1	Individual	Emil Adolf von Behring	1854-03-15	Hansdorf (Lawice)	Prussia (Poland)	Poland	Male	Marburg University	Marburg	Germany	POL
3	1901	Peace	The Nobel Peace Prize 1901	NaN	1/2	Individual	Frédéric Passy	1822-05-20	Paris	France	France	Male	NaN	NaN	NaN	FRA
4	1901	Peace	The Nobel Peace Prize 1901	NaN	1/2	Individual	Jean Henry Dunant	1828-05-08	Geneva	Switzerland	Switzerland	Male	NaN	NaN	NaN	CHE

	year	category	prize	motivation	prize_share	laureate_type	full_name	birth_date	birth_city	birth_country	birth_country_current	sex	organization_name	organization_city	organization_country	ISO
0	1901	Chemistry	The Nobel Prize in Chemistry 1901	"in recognition of the extraordinary services ...	1/1	Individual	Jacobus Henricus van 't Hoff	1852-08-30	Rotterdam	Netherlands	Netherlands	Male	Berlin University	Berlin	Germany	NLD
1	1901	Literature	The Nobel Prize in Literature 1901	"in special recognition of his poetic composit...	1/1	Individual	Sully Prudhomme	1839-03-16	Paris	France	France	Male	NaN	NaN	NaN	FRA
2	1901	Medicine	The Nobel Prize in Physiology or Medicine 1901	"for his work on serum therapy, especially its...	1/1	Individual	Emil Adolf von Behring	1854-03-15	Hansdorf (Lawice)	Prussia (Poland)	Poland	Male	Marburg University	Marburg	Germany	POL
3	1901	Peace	The Nobel Peace Prize 1901	NaN	1/2	Individual	Frédéric Passy	1822-05-20	Paris	France	France	Male	NaN	NaN	NaN	FRA
4	1901	Peace	The Nobel Peace Prize 1901	NaN	1/2	Individual	Jean Henry Dunant	1828-05-08	Geneva	Switzerland	Switzerland	Male	NaN	NaN	NaN	CHE
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
951	2020	Chemistry	The Nobel Prize in Chemistry 2020	“for the development of a method for genome ed...	1/2	Individual	Jennifer A. Doudna	1964-02-19	Washington, DC	United States of America	United States of America	Female	University of California	Berkeley, CA	United States of America	USA
950	2020	Chemistry	The Nobel Prize in Chemistry 2020	“for the development of a method for genome ed...	1/2	Individual	Emmanuelle Charpentier	1968-12-11	Juvisy-sur-Orge	France	France	Female	Max-Planck-Institut	Berlin	Germany	FRA
960	2020	Physics	The Nobel Prize in Physics 2020	“for the discovery of a supermassive compact o...	1/4	Individual	Reinhard Genzel	1952-03-24	Bad Homburg vor der Höhe	Germany	Germany	Male	University of California	Los Angeles, CA	United States of America	DEU
954	2020	Literature	The Nobel Prize in Literature 2020	“for her unmistakable poetic voice that with a...	1/1	Individual	Louise Glück	1943-04-22	New York, NY	United States of America	United States of America	Female	NaN	NaN	NaN	USA
961	2020	Physics	The Nobel Prize in Physics 2020	“for the discovery that black hole formation i...	1/2	Individual	Roger Penrose	1931-08-08	Colchester	United Kingdom	United Kingdom	Male	University of Oxford	Oxford	United Kingdom	GBR

	year	category	laureate_type	birth_date	full_name	organization_name
24	1904	Peace	Organization	NaN	Institut de droit international (Institute of ...	NaN
60	1910	Peace	Organization	NaN	Bureau international permanent de la Paix (Per...	NaN
89	1917	Peace	Organization	NaN	Comité international de la Croix Rouge (Intern...	NaN
200	1938	Peace	Organization	NaN	Office international Nansen pour les Réfugiés ...	NaN
215	1944	Peace	Organization	NaN	Comité international de la Croix Rouge (Intern...	NaN
237	1947	Peace	Organization	NaN	American Friends Service Committee (The Quakers)	NaN
238	1947	Peace	Organization	NaN	Friends Service Council (The Quakers)	NaN
283	1954	Peace	Organization	NaN	Office of the United Nations High Commissioner...	NaN
348	1963	Peace	Organization	NaN	Comité international de la Croix Rouge (Intern...	NaN
349	1963	Peace	Organization	NaN	Ligue des Sociétés de la Croix-Rouge (League o...	NaN
366	1965	Peace	Organization	NaN	United Nations Children's Fund (UNICEF)	NaN
399	1969	Peace	Organization	NaN	International Labour Organization (I.L.O.)	NaN
479	1977	Peace	Organization	NaN	Amnesty International	NaN
523	1981	Peace	Organization	NaN	Office of the United Nations High Commissioner...	NaN
558	1985	Peace	Organization	NaN	International Physicians for the Prevention of...	NaN
588	1988	Peace	Organization	NaN	United Nations Peacekeeping Forces	NaN
659	1995	Peace	Organization	NaN	Pugwash Conferences on Science and World Affairs	NaN
682	1997	Peace	Organization	NaN	International Campaign to Ban Landmines (ICBL)	NaN
703	1999	Peace	Organization	NaN	Médecins Sans Frontières	NaN
730	2001	Peace	Organization	NaN	United Nations (U.N.)	NaN
778	2005	Peace	Organization	NaN	International Atomic Energy Agency (IAEA)	NaN
788	2006	Peace	Organization	NaN	Grameen Bank	NaN
801	2007	Peace	Organization	NaN	Intergovernmental Panel on Climate Change (IPCC)	NaN
860	2012	Peace	Organization	NaN	European Union (EU)	NaN
873	2013	Peace	Organization	NaN	Organisation for the Prohibition of Chemical W...	NaN
897	2015	Peace	Organization	NaN	National Dialogue Quartet	NaN
919	2017	Peace	Organization	NaN	International Campaign to Abolish Nuclear Weap...	NaN
958	2020	Peace	Organization	NaN	World Food Programme (WFP)	NaN

	year	category	laureate_type	full_name	organization_name
1	1901	Literature	Individual	Sully Prudhomme	NaN
3	1901	Peace	Individual	Frédéric Passy	NaN
4	1901	Peace	Individual	Jean Henry Dunant	NaN
7	1902	Literature	Individual	Christian Matthias Theodor Mommsen	NaN
9	1902	Peace	Individual	Charles Albert Gobat	NaN
...	...	...	...	...	...
932	2018	Peace	Individual	Nadia Murad	NaN
942	2019	Literature	Individual	Peter Handke	NaN
946	2019	Peace	Individual	Abiy Ahmed Ali	NaN
954	2020	Literature	Individual	Louise Glück	NaN
958	2020	Peace	Organization	World Food Programme (WFP)	NaN

	year	category	laureate_type	full_name
18	1903	Physics	Individual	Marie Curie, née Sklodowska
62	1911	Chemistry	Individual	Marie Curie, née Sklodowska
89	1917	Peace	Organization	Comité international de la Croix Rouge (Intern...
215	1944	Peace	Organization	Comité international de la Croix Rouge (Intern...
278	1954	Chemistry	Individual	Linus Carl Pauling
283	1954	Peace	Organization	Office of the United Nations High Commissioner...
297	1956	Physics	Individual	John Bardeen
306	1958	Chemistry	Individual	Frederick Sanger
340	1962	Peace	Individual	Linus Carl Pauling
348	1963	Peace	Organization	Comité international de la Croix Rouge (Intern...
424	1972	Physics	Individual	John Bardeen
505	1980	Chemistry	Individual	Frederick Sanger
523	1981	Peace	Organization	Office of the United Nations High Commissioner...

Nobel Prize Analysis¶

Introduction¶

Import Statements¶

Notebook Presentation¶

Read the Data¶

Data Exploration & Cleaning¶

Check for Duplicates¶

Check for NaN Values¶

Convert Year and Birth Date to Datetime¶

Data Visualisation¶

Percentage of Male vs. Female Laureates¶

The Repeat Winners¶

Number of Prizes per Category¶

Male and Female Winners by Category¶

Number of Prizes Awarded Over Time¶

Are More Prizes Shared Than Before?¶

Top 20 Countries by Number of Prizes¶

Number of Prizes Won by Country¶

Top 20 Countries by Number of Prizes and Category¶

Number of Prizes Won by Each Country Over Time¶

Top 20 Research Institutions by Number of Prizes¶

Which Cities Make the Most Discoveries?¶

Birthplace of Nobel Laureates¶

Sunburst Chart: Country, City, and Organisation¶

Patterns in the Laureate Age at the Time of the Award¶

Age at Time of Award throughout History¶

Winning Age Across the Nobel Prize Categories¶

	organization_country	organization_city	organization_name	prize
205	United States of America	Cambridge, MA	Harvard University	29
280	United States of America	Stanford, CA	Stanford University	23
206	United States of America	Cambridge, MA	Massachusetts Institute of Technology (MIT)	21
209	United States of America	Chicago, IL	University of Chicago	20
195	United States of America	Berkeley, CA	University of California	19
...	...	...	...	...
110	Japan	Sapporo	Hokkaido University	1
111	Japan	Tokyo	Asahi Kasei Corporation	1
112	Japan	Tokyo	Kitasato University	1
113	Japan	Tokyo	Tokyo Institute of Technology	1
290	United States of America	Yorktown Heights, NY	IBM Thomas J. Watson Research Center	1

Nobel Prize Analysis¶

Introduction¶

Import Statements¶

Notebook Presentation¶

Read the Data¶

Data Exploration & Cleaning¶

Check for Duplicates¶

Check for NaN Values¶

Convert Year and Birth Date to Datetime¶

Add a Column with the Prize Share as a Percentage¶

Data Visualisation¶

Percentage of Male vs. Female Laureates¶

The Repeat Winners¶

Number of Prizes per Category¶

Male and Female Winners by Category¶

Number of Prizes Awarded Over Time¶

Are More Prizes Shared Than Before?¶

Top 20 Countries by Number of Prizes¶

Number of Prizes Won by Country¶

Top 20 Countries by Number of Prizes and Category¶

Number of Prizes Won by Each Country Over Time¶

Top 20 Research Institutions by Number of Prizes¶

Which Cities Make the Most Discoveries?¶

Birthplace of Nobel Laureates¶

Sunburst Chart: Country, City, and Organisation¶

Patterns in the Laureate Age at the Time of the Award¶

Age at Time of Award throughout History¶

Winning Age Across the Nobel Prize Categories¶